Effective level set image segmentation with a kernel induced data term

  • Authors:

  • Mohamed Ben Salah;Amar Mitiche;Ismail Ben Ayed

  • Affiliations:

  • Institut National de la Recherche Scientifique, EMT, Montréal, QC, Canada;Institut National de la Recherche Scientifique, EMT, Montréal, QC, Canada;General Electric Canada, London, ON, Canada

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2010

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Abstract

This study investigates level set multiphase image segmentation by kernel mapping and piecewise constant modeling of the image data thereof. A kernel function maps implicitly the original data into data of a higher dimension so that the piecewise constant model becomes applicable. This leads to a flexible and effective alternative to complex modeling of the image data. The method uses an active curve objective functional with two terms: an original term which evaluates the deviation of the mapped image data within each segmentation region from the piecewise constant model and a classic length regularization term for smooth region boundaries. Functional minimization is carried out by iterations of two consecutive steps: 1) minimization with respect to the segmentation by curve evolution via Euler-Lagrange descent equations and 2) minimization with respect to the regions parameters via fixed point iterations. Using a common kernel function, this step amounts to a mean shift parameter update. We verified the effectiveness of the method by a quantitative and comparative performance evaluation over a large number of experiments on synthetic images, as well as experiments with a variety of real images such as medical, satellite, and natural images, as well as motion maps.